Anoop kumar

@PCMohanMP Sir why kadugodi flyover work is not moving?

🚨Bengaluru Suburban Rail Project faced delays due to pending private land acquisition and delayed transfer of State Govt land by the Govt of Karnataka. With these constraints easing, land for Corridor 2 is secured, and Railways has approved alignments for all 4 corridors.

I have 12 years of experience and working as a Principal Engineer @Atlassian and I have seen concurrency scaring the hell out of a lot of junior engineers. It’s one of the most feared topics in system design & backend interviews — race conditions, deadlocks, thread pools… you name it. But once you internalize these 20 must-know concepts, everything clicks. Save this thread. Read till the end. Your future interviews and production systems will thank you.

This 2 hour Stanford lecture will teach you more about how LLMs like ChatGPT & Claude are built than most people working at top AI companies learn in their entire careers. Bookmark this & give 2 hours today, no matter what. It'll be the most productive thing you do this week.

Learn AI for free directly from top companies 𝟭 - 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰: anthropic.skilljar.com 𝟮 - 𝗚𝗼𝗼𝗴𝗹𝗲: grow.google/ai 𝟯 - 𝗠𝗲𝘁𝗮: ai.meta.com/resources/ 𝟰 - 𝗡𝗩𝗜𝗗𝗜𝗔: developer.nvidia.com/cuda 𝟱 - 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁: learn.microsoft.com/en-us/training/ 𝟲 - 𝗢𝗽𝗲𝗻𝗔𝗜: academy.openai.com 𝟳 - 𝗜𝗕𝗠: skillsbuild.org 𝟴 - 𝗔𝗪𝗦: skillbuilder.aws 𝟵 - 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝗔𝗜: deeplearning.ai 𝟭𝟬 - 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: huggingface.co/learn

Let’s level up your Azure skills this week. If your level is: • Beginner: Create an AI agent → Microsoft Applied Skills: Create an AI agent • Intermediate: Build a generative AI chat app → Microsoft Applied Skills: Build a generative AI chat app • Advanced: Develop generative AI apps with Azure OpenAI and SK → Microsoft Applied Skills: Develop generative AI apps with Azure OpenAI and Semantic Kernel

If you want to get Microsoft AI certified, start here: • Level 1: Azure AI Fundamentals (AI-900) • Level 2: Azure AI Engineer Associate (AI-102) • Level 3: Azure Solutions Architect Expert (AZ-305) (not AI‑specific, but useful for architecting AI solutions)

This man is a real-life hero 😳🙌 risking everything just to save a dog 🐶❤️

Instead of Netflix, watch this Stanford lecture on AI scaling bottlenecks

You don't need random papers, just learn these concepts sequentially to kickstart your LLM engineer journey. Here’s what actually matters if you’re an engineer: - Tokenization and embeddings - Attention and transformer blocks - Training and fine tuning - LoRA and QLoRA - DPO and alignment - Quantization - KV cache and inference systems - FlashAttention and PagedAttention Not theory. Systems. I wrote a complete guide covering everything step by step. If you want to build with LLMs, not just study them, this is for you. Also breaking down inference and deployment at scale in upcoming posts on hands-on level.

Stop wasting hours trying to learn AI. 📘📚 I have already done it for you. With one list. Zero confusion. And no fluff 📹 Videos: 1. LLM Introduction: t.co/kyDon6qLrb 2. LLMs from Scratch: t.co/2hyMhuKoiI 3. Agentic AI Overview (Stanford): t.co/FXu6cAqITC 4. Building and Evaluating Agents: t.co/ZigR1tdOFL 5. Building Effective Agents: t.co/uYwfwO55mO 6. Building Agents with MCP: t.co/4arFTW1b3i 7. Building an Agent from Scratch: t.co/eOmveyM9Hz 8. Philo Agents: t.co/zLu7x1tx9m 🗂️ Repos 1. GenAI Agents: t.co/eXCl2YaRPv 2. Microsoft's AI Agents for Beginners: t.co/3CSW4zPAwf 3. Prompt Engineering Guide: t.co/GVzvxPYDVO 4. Hands-On Large Language Models: t.co/0rgDvhx3pI 5. AI Agents for Beginners: t.co/3CSW4zPAwf 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: t.co/9z5KHF9DMe 8. Hands-On AI Engineering:t.co/dldAj5Xkr6 9. Awesome Generative AI Guide: t.co/U2WZhT4ERV 10. Designing Machine Learning Systems: t.co/sYAZX34YdQ 11. Machine Learning for Beginners from Microsoft: t.co/NjFxHbC9jZ 12. LLM Course: t.co/N34YTPu1OK 🗺️ Guides 1. Google's Agent Whitepaper: t.co/bW3Ov3vMW0 2. Google's Agent Companion: t.co/wredwWAbBA 3. Building Effective Agents by Anthropic: t.co/fxtE4alVrJ. 4. Claude Code Best Agentic Coding practices: t.co/lLSwJ9pG7C 5. OpenAI's Practical Guide to Building Agents: t.co/xgkEIogGfh 📚Books: 1. Understanding Deep Learning: t.co/CjcKpTemmV 2. Building an LLM from Scratch: t.co/DaWBxOx8o3 3. The LLM Engineering Handbook: t.co/ZA1n0N41Mf 4. AI Agents: The Definitive Guide - Nicole Koenigstein: t.co/boLkl1VlKb 5. Building Applications with AI Agents - Michael Albada: t.co/H1Xf5EkJLL 6. AI Agents with MCP - Kyle Stratis: t.co/JI3ELQZE6a 7. AI Engineering: t.co/Xk0JzMIf7o 📜 Papers 1. ReAct: t.co/QNqE4UU55w 2. Generative Agents: t.co/CwEpoJgY1U. 3. Toolformer: t.co/5m9xZd5teZ 4. Chain-of-Thought Prompting: t.co/KjVlgdWi77. 🧑🏫 Courses: 1. HuggingFace's Agent Course: t.co/7FSUYKxIdG 2. MCP with Anthropic: t.co/IkZGiWm2yS 3. Building Vector Databases with Pinecone: t.co/2YRoMfLdXd 4. Vector Databases from Embeddings to Apps: t.co/23A50ixbHJ 5. Agent Memory: t.co/uc3L9BrNF7 Repost for your network ♻️

Andrew Ng dropped absolute gold on AI careers

Stanford's FREE Courses on AI & ML: ❯ CS221 - Artificial Intelligence ❯ CS229 - Machine Learning ❯ CS230 - Deep Learning ❯ CS234 - Reinforcement Learning ❯ CS224N - NLP with Deep Learning ❯ CS336 - LLM from Scratch All course links inside:

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Tech workers realizing that they could have kept high pay, job stability and remote work if they just stopped making cringe “Day in my Life” videos on TikTok

This 2 hour Stanford lecture on AI careers will teach you more about winning in the AI race than every piece of AI content you have scrolled past this year. Bookmark this & give it 2 hours, no matter what. It'll be the most productive thing you could do this weekend.

Only Mr. Nags can roast Virat Kohli and get away with it!😭😂

I recreated Ramayana teaser with our OG RAM, using Seedance 2.0 on @higgsfield

Someone on Github Uploaded Company wise Interview Questions 😭😭 I'm going to cancel my leetcode premium now 😭🙂